store = {}
store['args']={'num_inference_samples': 10, 'available_sample_k': 5, 'seed': 507556, 'type': 'AcquisitionFunction.random', 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST_balanced with random acquisition', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'initial_percentage': 50, 'reduce_percentage': 10, 'min_remaining_percentage': 30, 'min_candidates_per_acquired_item': 100, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 'emnist_balanced_independent_random_k10_b5_507556', 'experiments_laaos': './experiment_configs/emnist_random/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=emnist_balanced_independent_random_k10_b5_507556', '--experiments_laaos=./experiment_configs/emnist_random/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.023829787234042554, 'nll': 3.861640013966153}, 'chosen_samples': [14529, 77064, 104545, 88311, 24480], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 25.441144841000096, 'batch_acquisition_elapsed_time': 0.009464358000059292})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.0601063829787234, 'nll': 42.813951276594004}, 'chosen_samples': [53265, 102416, 89837, 39793, 73174], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.279870743999936, 'batch_acquisition_elapsed_time': 0.0026837149999892063})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.09329787234042553, 'nll': 51.025283959721}, 'chosen_samples': [6561, 60174, 43838, 51870, 91280], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.934586384, 'batch_acquisition_elapsed_time': 0.0027030830000285277})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11436170212765957, 'nll': 43.90811854566031}, 'chosen_samples': [19892, 94542, 24850, 45268, 39227], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.28093955399993, 'batch_acquisition_elapsed_time': 0.00255382099999224})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14138297872340425, 'nll': 38.064979239901334}, 'chosen_samples': [78533, 53210, 12297, 85531, 96356], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.986647834999985, 'batch_acquisition_elapsed_time': 0.0027607270000089557})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.15085106382978725, 'nll': 33.532619314949564}, 'chosen_samples': [16178, 91856, 35237, 12151, 22249], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 49.501816477000034, 'batch_acquisition_elapsed_time': 0.00289019299998472})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.17356382978723403, 'nll': 30.05804603313512}, 'chosen_samples': [37893, 32525, 94798, 57906, 80149], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 49.45181484099999, 'batch_acquisition_elapsed_time': 0.0027394179999191692})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18824468085106383, 'nll': 27.787892815663454}, 'chosen_samples': [110656, 108568, 53195, 4336, 75718], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.49570412800017, 'batch_acquisition_elapsed_time': 0.0028364629999941826})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21351063829787234, 'nll': 21.57873565664824}, 'chosen_samples': [16408, 80568, 24999, 51137, 46817], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.900633999999854, 'batch_acquisition_elapsed_time': 0.0026799259999279457})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.21611702127659574, 'nll': 22.99324487678295}, 'chosen_samples': [3627, 10318, 105901, 32282, 69035], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 50.27937679600018, 'batch_acquisition_elapsed_time': 0.002716227000064464})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23829787234042554, 'nll': 21.193605482641683}, 'chosen_samples': [26591, 106390, 85955, 31638, 46897], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.9540781180001, 'batch_acquisition_elapsed_time': 0.0026245199999266333})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23372340425531915, 'nll': 20.527136679825}, 'chosen_samples': [60757, 79468, 93140, 93282, 30354], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.884066273999906, 'batch_acquisition_elapsed_time': 0.00273354199998721})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.24111702127659573, 'nll': 19.93816754393057}, 'chosen_samples': [56312, 19082, 5326, 89971, 35680], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.61034341599998, 'batch_acquisition_elapsed_time': 0.002726670000129161})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2495212765957447, 'nll': 19.41854610295752}, 'chosen_samples': [17085, 10827, 7706, 31219, 84650], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.27638897299994, 'batch_acquisition_elapsed_time': 0.002674289999959001})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23691489361702128, 'nll': 19.47234420075759}, 'chosen_samples': [75238, 2572, 23381, 27502, 53881], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.046906389000014, 'batch_acquisition_elapsed_time': 0.0028689260000192007})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2495744680851064, 'nll': 17.94767841658758}, 'chosen_samples': [14061, 78811, 109769, 22957, 16336], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.742536023999946, 'batch_acquisition_elapsed_time': 0.002893917999926998})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.256063829787234, 'nll': 18.136572323613347}, 'chosen_samples': [38155, 99724, 7433, 67360, 92503], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.10188479599992, 'batch_acquisition_elapsed_time': 0.0029688330000681162})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2548404255319149, 'nll': 16.630401365238935}, 'chosen_samples': [46460, 93019, 20207, 64887, 92507], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.91788816300004, 'batch_acquisition_elapsed_time': 0.002668521999794393})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2818617021276596, 'nll': 16.478281330584217}, 'chosen_samples': [80104, 19511, 34315, 4937, 13260], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.5173256999999, 'batch_acquisition_elapsed_time': 0.0028279759999350063})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.28393617021276596, 'nll': 14.949223487089915}, 'chosen_samples': [40491, 59205, 4588, 11048, 69673], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 50.2719968209999, 'batch_acquisition_elapsed_time': 0.0024920949999795994})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28414893617021275, 'nll': 13.783525771912107}, 'chosen_samples': [71756, 29221, 16337, 98840, 77587], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.844194803999926, 'batch_acquisition_elapsed_time': 0.0024695530000826693})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.30138297872340425, 'nll': 13.1183136072609}, 'chosen_samples': [66427, 28007, 29130, 82155, 87238], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 48.88641000300004, 'batch_acquisition_elapsed_time': 0.002708504000111134})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3180851063829787, 'nll': 12.484090491644242}, 'chosen_samples': [16114, 111618, 68793, 11441, 2043], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.265148670999906, 'batch_acquisition_elapsed_time': 0.0027171669999006554})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3031382978723404, 'nll': 12.717805532870772}, 'chosen_samples': [16514, 104792, 94259, 73848, 44493], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.679051414000014, 'batch_acquisition_elapsed_time': 0.0028032010000060836})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3158510638297872, 'nll': 12.041656674099732}, 'chosen_samples': [5338, 28584, 103868, 43317, 70663], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.56716310699994, 'batch_acquisition_elapsed_time': 0.0024152739999863115})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.313563829787234, 'nll': 11.529932225751118}, 'chosen_samples': [24579, 75105, 110598, 62002, 79091], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.34220823600003, 'batch_acquisition_elapsed_time': 0.00266948900002717})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3291489361702128, 'nll': 9.649016497407507}, 'chosen_samples': [95279, 92841, 11585, 61576, 33635], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.366755370999954, 'batch_acquisition_elapsed_time': 0.00273871100012002})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3172872340425532, 'nll': 9.92736651747087}, 'chosen_samples': [106053, 107985, 108742, 6495, 146], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.70157963399993, 'batch_acquisition_elapsed_time': 0.0028527220001706155})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3504255319148936, 'nll': 9.167599715856165}, 'chosen_samples': [57643, 85021, 51782, 46343, 52012], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.551253661000146, 'batch_acquisition_elapsed_time': 0.002672329000006357})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.33595744680851064, 'nll': 8.972435178086164}, 'chosen_samples': [82260, 37724, 81003, 90955, 112747], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.194475768000075, 'batch_acquisition_elapsed_time': 0.0026898459998392354})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3447340425531915, 'nll': 8.708865132309654}, 'chosen_samples': [65478, 63092, 90694, 46065, 99907], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.70807505300013, 'batch_acquisition_elapsed_time': 0.0028067870002814743})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.37042553191489364, 'nll': 7.989091368659379}, 'chosen_samples': [8094, 101767, 10150, 106537, 91693], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.2869055860001, 'batch_acquisition_elapsed_time': 0.002881719000015437})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3676595744680851, 'nll': 8.1386805928787}, 'chosen_samples': [101547, 102411, 106314, 107172, 277], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.66346076100035, 'batch_acquisition_elapsed_time': 0.0027903089999199437})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35973404255319147, 'nll': 8.42974237593438}, 'chosen_samples': [31477, 106598, 34051, 818, 78794], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.139626474000124, 'batch_acquisition_elapsed_time': 0.0027845820000038657})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.386968085106383, 'nll': 7.993148772202274}, 'chosen_samples': [48988, 87803, 1866, 110196, 110316], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.815136765999796, 'batch_acquisition_elapsed_time': 0.00253818599958322})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.39659574468085107, 'nll': 7.136416349652602}, 'chosen_samples': [55351, 83759, 65620, 48542, 22673], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.59070733600038, 'batch_acquisition_elapsed_time': 0.002699118000236922})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.39643617021276595, 'nll': 6.761047792996498}, 'chosen_samples': [19299, 102067, 53784, 88615, 51215], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.70672794700022, 'batch_acquisition_elapsed_time': 0.0025465899998380337})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4004255319148936, 'nll': 6.751479297382085}, 'chosen_samples': [64809, 42301, 24297, 12523, 99154], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.15967790000013, 'batch_acquisition_elapsed_time': 0.0024243050002041855})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3972340425531915, 'nll': 6.176613400638738}, 'chosen_samples': [59553, 64364, 110207, 50598, 241], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.17676605500037, 'batch_acquisition_elapsed_time': 0.0026374060003035993})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4221276595744681, 'nll': 5.945313748331226}, 'chosen_samples': [8349, 7057, 11282, 23266, 108174], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.95170734900012, 'batch_acquisition_elapsed_time': 0.0024086859998533328})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.414468085106383, 'nll': 6.072020732013785}, 'chosen_samples': [36531, 21630, 83209, 7993, 34172], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.78167252100002, 'batch_acquisition_elapsed_time': 0.002465814000061073})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.43079787234042555, 'nll': 5.834737369411802}, 'chosen_samples': [14745, 21581, 46968, 7123, 4951], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.16011603600009, 'batch_acquisition_elapsed_time': 0.0026347120001446456})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.425, 'nll': 5.793127449679566}, 'chosen_samples': [102677, 1641, 91427, 68888, 23318], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.43572196100013, 'batch_acquisition_elapsed_time': 0.0026905420004368352})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4346276595744681, 'nll': 5.558145247462581}, 'chosen_samples': [31346, 22289, 91720, 15386, 112081], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.190084995000234, 'batch_acquisition_elapsed_time': 0.0025408380001863407})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.44005319148936173, 'nll': 5.185910976200664}, 'chosen_samples': [106748, 49677, 39645, 79436, 88300], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.04114055400032, 'batch_acquisition_elapsed_time': 0.002502115999959642})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4471276595744681, 'nll': 4.9949651018365255}, 'chosen_samples': [93782, 57724, 90665, 33520, 86125], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.1669341060001, 'batch_acquisition_elapsed_time': 0.0027368559999558784})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.44813829787234044, 'nll': 5.111940900063578}, 'chosen_samples': [105756, 9048, 98737, 103594, 22758], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.587418222999986, 'batch_acquisition_elapsed_time': 0.002594577000309073})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4625531914893617, 'nll': 4.877880210403115}, 'chosen_samples': [38567, 61489, 27112, 1359, 97529], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.319555476999994, 'batch_acquisition_elapsed_time': 0.0025716950003698003})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.46356382978723404, 'nll': 5.065492741639945}, 'chosen_samples': [61848, 70305, 85029, 74330, 94815], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.04985741499968, 'batch_acquisition_elapsed_time': 0.0024865509999472124})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4652659574468085, 'nll': 5.052646548395779}, 'chosen_samples': [62345, 39261, 108459, 25813, 47458], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.19867119799983, 'batch_acquisition_elapsed_time': 0.0027307279997330625})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4588829787234043, 'nll': 4.918863734638754}, 'chosen_samples': [73827, 20403, 50629, 89373, 2213], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.43235714000002, 'batch_acquisition_elapsed_time': 0.002733522000198718})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4665957446808511, 'nll': 4.788613799144295}, 'chosen_samples': [20759, 14207, 35272, 75633, 75545], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.25604260599994, 'batch_acquisition_elapsed_time': 0.0025128940001195588})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4676595744680851, 'nll': 4.652367901981195}, 'chosen_samples': [67839, 54807, 11515, 67066, 70058], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.66728964399999, 'batch_acquisition_elapsed_time': 0.0027200760000596347})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.469468085106383, 'nll': 4.325039197639582}, 'chosen_samples': [2152, 67163, 8525, 104747, 92474], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.34609755400015, 'batch_acquisition_elapsed_time': 0.0024441559999104356})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.46074468085106385, 'nll': 4.723939476595915}, 'chosen_samples': [19612, 72992, 79394, 30235, 17875], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.64073540799973, 'batch_acquisition_elapsed_time': 0.0028231569999661588})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.48021276595744683, 'nll': 4.2515415212467635}, 'chosen_samples': [43530, 58809, 103872, 95010, 98282], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.16202822900004, 'batch_acquisition_elapsed_time': 0.0024457340000481054})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4777127659574468, 'nll': 4.880066901238677}, 'chosen_samples': [81495, 108402, 50468, 11007, 16716], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.24782936600013, 'batch_acquisition_elapsed_time': 0.0025791359998947883})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4777127659574468, 'nll': 4.755344877269277}, 'chosen_samples': [36760, 64633, 102690, 87943, 11104], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.01604276699982, 'batch_acquisition_elapsed_time': 0.002564902999893093})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.47, 'nll': 4.627250882477995}, 'chosen_samples': [80703, 91, 90157, 61817, 2598], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.91574664500013, 'batch_acquisition_elapsed_time': 0.0027321809998284152})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.49117021276595746, 'nll': 4.161389684352627}, 'chosen_samples': [15347, 3680, 38628, 49176, 64226], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.817208821999884, 'batch_acquisition_elapsed_time': 0.0025603620001675154})
